Computer modeling
Computer modeling

Advancing urban tree monitoring with AI-powered digital twins

The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

Modeling relationships to solve complex problems efficiently

Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.

Machine learning unlocks secrets to advanced alloys

An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.

Making climate models relevant for local decision-makers

A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.

Scientists use generative AI to answer complex questions in physics

A new technique that can automatically classify phases of physical systems could help scientists investigate novel materials.

To build a better AI helper, start by modeling the irrational behavior of humans

A new technique can be used to predict the actions of human or AI agents who behave suboptimally while working toward unknown goals.

Using deep learning to image the Earth’s planetary boundary layer

Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth’s surface. Their techniques could improve weather and drought prediction.

MIT-derived algorithm helps forecast the frequency of extreme weather

The new approach “nudges” existing climate simulations closer to future reality.

Engineering household robots to have a little common sense

With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.

Generative AI for smart grid modeling

MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.